Following tables show, by sample, the number, mean length and length standard deviation of R1 and R2 reads.
QC was performed with Trimmomatic and PRINSEQ.
Following tables show, by sample, the number, mean length and length standard deviation of R1 and R2 reads.
Following table shows the number of reads, average length and standard deviation for joined and unjoined reads using FLASH.
Following table shows the Sequence Type (ST), MLST allelic profile and Clonal Complex (CC) for each isolate using mlst and the PubMLST database.
Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).
Following table shows the number of contigs (> 200 bp), genome length, average contig length, N50, GC content (%) and depth coverage using SPAdes and annotation information using Prokka for each draft genome.
Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).
Following table shows antibiotic resistance genes by screening the draft genomes against the Pathogen Detection Reference Gene Catalog by using AMRFinderPlus. Any hit with coverage below 80 % and identity below 60 % was removed.
Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).
Following figure represents presence/absence of antibiotic resistance genes in each draft genome against the Pathogen Detection Reference Gene Catalog by using AMRFinderPlus. Presence is represented as minimum coverage of 80 % and minimum identity of 60 %.
Following table shows known point mutations by screening the draft genomes against the Pathogen Detection Reference Gene Catalog by using AMRFinderPlus. Any hit with coverage below 80 % and identity below 60 % was removed.
Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).
Following figure represents known point mutations in each draft genome by using AMRFinderPlus. Presence is represented as minimum coverage of 80 % and minimum identity of 60 %.
Following table(s) show(s) antibiotic resistance genes by screening the draft genomes against the selected database(s) by using ABRicate. Any hit with coverage below 80 % and identity below 60 % was removed.
Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).
Following figure(s) represent(s) presence/absence of antibiotic resistance genes in each draft genome against the selected database(s) by using ABRicate. Presence is represented as minimum coverage of 80 % and minimum identity of 60 %.
Following table(s) show(s) virulence genes by screening the draft genomes against the selected database(s) by using ABRicate. Any hit with coverage below 80 % and identity below % was removed.
Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).
Following figure represents presence/absence of virulence genes in each draft genome against the selected database(s) by using ABRicate. Presence is represented as minimum coverage of 80 % and minimum identity of 60 %.
Following table shows virulence genes by screening the draft genomes against the inhouse VFDB database using BLAST. Any hit with identity below 50 % was removed.
Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).
Following figure represents presence/absence of virulence genes in each draft genome against the inhouse VFDB database using BLAST. Presence is represented as minimum coverage of 80 % and minimum identity of 60 %.
Following charts show pangenome analysis with minimum 95 % identity for blastp using Roary.
Binary heatmap shows the presence (dark grey) and absence (white) of genes. Phylogeny for each isolate is shown on the left and was constructed based on accesory genes from the pangenome.
Following table shows genomic variants in each draft genome against the reference genome you indicated using Snippy.
Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).
Following table shows summary information for each draft genome.
Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).
Irene Ortega-Sanz, Jose A. Barbero and Antonio Canepa. CamPype (2022). Available at https://github.com/JoseBarbero/CamPype
Following packages and tools were used in CamPype:
| Package/Tool | Reference |
|---|---|
| Trimmomatic | A.M. Bolger et al., 2014 |
| Prinseq | R. Schmieder and R. Edwards, 2011 |
| FLASH | T. Magoc and S. Salzberg, 2011 |
| SPAdes | A. Bankevich et al., 2012 |
| QUAST | A. Gurevich et al., 2013 |
| progressiveMauve | A.E. Darling et al., 2010 |
| mlst | T. Seemann |
| Abricate | T. Seemann |
| BLAST | Z. Zhang et al., 2000 |
| AMRFinderPlus | M. Feldgarden et al., 2019 |
| Prokka | T. Seemann, 2014 |
| DFAST | Y. Tanizawa et al., 2018 |
| Roary | A.J. Page et al., 2015 |
| snippy | T. Seemann |
| ape | Paradis and Schliep, 2019 |
| complexHeatmap | Gu et al., 2016 |
| dplyr | Wickham et al., 2022 |
| DT | Xie et al., 2022 |
| ggplot2 | Wickham, 2016 |
| ggtree | Yu et al., 2017 |
| pander | Daróczi and Tsegelskyi, 2022 |
| phytools | Revell, 2012 |
| plotly | Sievert, 2020 |
| rjson | Couture-Beil, 2022 |
| rmarkdown | Allaire et al., 2022 |
| tidyverse | Wickham et al., 2019 |